import matplotlib
matplotlib.use('TkAgg')
import numpy as np
from numpy import mean
from tkinter import filedialog as FD
from scipy import stats
import plotly.plotly as py
import matplotlib.pyplot as plt
from matplotlib import pylab



filename = FD.askopenfilename()
print(filename)
# opening *into an object* file to read
file = open(filename, "r")

# file.readline = reading the file line by line (.read is everything)
title = file.readline()
# assign title to substring
title = title[7:]

file.readline()
# read date
date = file.readline()
date = date[16:]

file.readline()
file.readline()
file.readline()

print(title)
print(date)

# make blank list, read
blanks = []

keep_going = True
current_line = file.readline()

while keep_going == True:
    if current_line == "\n":
        keep_going = False
    else:
        blanks.append(float(current_line))
        current_line = file.readline()

print(blanks)

# reads the blanks
file.readline()
file.readline()

keep_going = True
current_line = file.readline()

# making two arrays
x = []
y = []

# making a loop for the table
while keep_going:
    if current_line == "":
        keep_going = False
    else:
        li = current_line.split("\t")

        xi = float(li[0])
        for yi in li[2:]:
            x.append(xi)
            y.append(float(yi))
        current_line = file.readline()

x = np.array(x, dtype=np.float64)
y = np.array(y, dtype=np.float64)
print(x)
print(y)

# generate linear fit will say a comment and a website will show up, please ignore. This code came from a website.
# as well as my coding. Not sure how to get rid of the website or comment
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
line = slope * x + intercept

plt.plot(x,y, line)
pylab.title('Linear Fit with Matplotlib')
ax = plt.gca()

fig = plt.gcf()
py.plot_mpl(fig, filename='linear-Fit-with-matplotlib')

fig, ax = plt.subplots(figsize=(5,4))


def best_fit_slope_and_intercept(xArray, yArray):
    m = (((mean(xArray) * mean(yArray)) - mean(yArray * xArray)) /
         ((mean(xArray) * mean(xArray)) - mean(xArray * xArray)))

    b = mean(yArray) - m * mean(xArray)

    print("STD deviation of blank=", np.std(blanks))
    print("LOD = ", np.std(blanks / m) * 3)

    return m, b


print("best_fit result: ", best_fit_slope_and_intercept(x, y))
m, b = best_fit_slope_and_intercept(x, y)

Fit_line = [(m * xpt) + b for xpt in x]
plt.scatter(x, y, color='#003F72')
plt.plot(x, Fit_line)
plt.show()
main.mainloop()